This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.0.2.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

⏪ | 🏠 | ⏩

Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Dredge FishDred 2010-2014 21 Mactromeris polynyma
Net FishNet 2010 5 Clupea harengus, Gadus morhua
Trap FishTrap 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl FishTraw 2013-2014 2 Pandalus borealis

1. Spatial variation of exposure indices

Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).

Aquaculture
## Model selected: Sph
## nugget = 0; sill = 0.00704; range = 7.01955; kappa = 0.5

City
## Model selected: Lin
## nugget = 0.00025; sill = 0.00602; range = 8.57222; kappa = 0.5

Sediment dredging
## Model selected: Exp
## nugget = 0.00021; sill = 0.02042; range = 4.52941; kappa = 0.5

Industry
## Model selected: Sph
## nugget = 1e-04; sill = 0.0072; range = 10.10924; kappa = 0.5

Sewers
## Model selected: Exp
## nugget = 0; sill = 0.03366; range = 43.15003; kappa = 0.5

Shipping
## Model selected: Lin
## nugget = 0; sill = 0.06455; range = 4.27615; kappa = 0.5

Fisheries
## Model selected: Lin
## nugget = 0; sill = 0.02461; range = 3.40362; kappa = 0.5

2. Relationships with abiotic parameters

2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.

Aquaculture

City

Sediment dredging

Industry

Sewers

Shipping

Fisheries

Cumulative exposure

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture -0.439 0.167 0.477 -0.444 -0.048 -0.688 -0.784 -0.737 -0.668 -0.62 -0.772 -0.766 -0.722 -0.733 0.311 0 -0.029 0.345 0.188
city -0.155 -0.067 0.427 -0.273 -0.096 -0.246 -0.163 -0.171 0.086 -0.004 -0.154 -0.243 -0.167 -0.015 -0.108 -0.036 -0.153 -0.055 0.035
dredging 0.275 -0.084 -0.091 0.103 0.055 0.264 0.19 0.407 0.574 0.649 0.55 0.219 0.324 0.482 -0.215 -0.133 0.049 -0.13 -0.023
industry 0.159 -0.071 -0.016 0.045 0.069 0.176 0.115 0.348 0.514 0.588 0.504 0.157 0.253 0.405 -0.246 -0.115 0.053 -0.198 -0.076
sewers 0.254 -0.037 -0.313 0.268 0.249 0.609 0.581 0.654 0.694 0.591 0.707 0.579 0.689 0.689 -0.353 -0.063 0.021 -0.369 -0.174
shipping 0.456 -0.249 -0.291 0.314 -0.015 0.537 0.504 0.618 0.693 0.677 0.708 0.549 0.576 0.687 -0.19 -0.06 0.022 -0.172 -0.095
fisheries -0.495 0.202 0.378 -0.38 -0.138 -0.569 -0.542 -0.554 -0.608 -0.578 -0.587 -0.54 -0.564 -0.614 0.308 0.173 -0.064 0.222 -0.017
cumulative_exposure 0.261 -0.108 -0.114 0.161 0.053 0.28 0.158 0.306 0.441 0.464 0.397 0.209 0.327 0.405 -0.055 -0.045 -0.004 -0.065 -0.1
p-values of correlation test between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture 2.038e-06 0.08404 1.82e-07 1.453e-06 0.6241 1.878e-16 1.123e-23 1.028e-19 2.749e-15 8.733e-13 1.446e-22 4.639e-22 1.119e-18 1.952e-19 0.001047 0.999 0.7653 0.0002525 0.05113
city 0.1087 0.4921 3.982e-06 0.004225 0.3206 0.01043 0.09275 0.07674 0.3781 0.964 0.1118 0.01126 0.08362 0.8744 0.2674 0.7138 0.1134 0.5708 0.7171
dredging 0.004038 0.3876 0.3492 0.2869 0.574 0.005687 0.04861 1.217e-05 8.309e-11 2.962e-14 6.813e-10 0.02309 0.000633 1.283e-07 0.02517 0.171 0.6121 0.1789 0.8096
industry 0.1007 0.465 0.8689 0.6459 0.4811 0.06919 0.2347 0.0002275 1.3e-08 2.144e-11 2.783e-08 0.1043 0.008203 1.389e-05 0.01022 0.236 0.5892 0.03971 0.4341
sewers 0.007974 0.702 0.000962 0.004998 0.009281 2.623e-12 4.439e-11 1.762e-14 8.768e-17 1.703e-11 1.192e-17 5.084e-11 1.659e-16 1.805e-16 0.000176 0.5189 0.8284 8.325e-05 0.07195
shipping 7.165e-07 0.009324 0.002213 0.0009345 0.8743 2.146e-09 2.655e-08 1.041e-12 1.003e-16 9.205e-16 1.105e-17 7.68e-10 7.258e-11 2.359e-16 0.04853 0.5351 0.8202 0.07554 0.3296
fisheries 5.243e-08 0.03592 5.386e-05 4.872e-05 0.1551 1.322e-10 1.395e-09 5.146e-10 3.019e-12 5.829e-11 2.539e-11 1.593e-09 2.004e-10 1.555e-12 0.001162 0.07378 0.5101 0.02081 0.8638
cumulative_exposure 0.006326 0.267 0.2382 0.09549 0.5853 0.003373 0.1026 0.001274 1.82e-06 4.227e-07 2.109e-05 0.02966 0.000561 1.372e-05 0.5715 0.6442 0.9694 0.5029 0.3047

3. Relationships with benthic communities

The most abundant taxa in our study area are:

  • Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
  • Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)

The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density or biomass.

Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 2.01, and the five categories are from ‘bad’ to ‘high’, with 20 %, 40 %, 60 % or 80 % of the maximum exposure.

By exposure gradient

By exposure categories

Phylum mean density by group
Phylum low bad moderate high good
Annelida 15.2 28.1 36.4 29.7 16.5
Arthropoda 13.4 41.6 54.9 44.2 3.5
Cnidaria 0 0 0 0 0.5
Echinodermata 0.2 0.273 6 0.827 56.5
Mollusca 12 9.5 19.3 12.8 9.5
Nematoda 0 0.364 4.93 16.3 26.5
Nemertea 0 0.182 0 0.231 0
Sipuncula 0.4 0.455 0.333 0.154 0
Phylum mean biomass by group
Phylum low bad moderate high good
Annelida 3.2 0.927 2.23 0.732 0.121
Arthropoda 0.0221 0.0705 0.11 0.167 0.0527
Cnidaria 0 0 0 0 1.68
Echinodermata 0.00436 3.68 2.53 6.57 53.5
Mollusca 1.8 0.234 2.62 1.3 0.572
Nematoda 0 3.64e-05 0.000393 0.00069 0.00085
Nemertea 0 0.0777 0 4.23e-05 0
Sipuncula 0.0168 0.0191 0.00497 0.00878 0

4. Relationships with community characteristics

The following graphs present the distribution of community characteristics along index of cumulative exposure.

4.1. Data manipulation

For the following analyses, independant variables are exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.

All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
  aquaculture city dredging industry sewers shipping fisheries
aquaculture 1 0.061 -0.355 -0.299 -0.666 -0.696 0.718
city 0.061 1 0.334 0.325 0.131 0.22 -0.201
dredging -0.355 0.334 1 0.961 0.668 0.686 -0.471
industry -0.299 0.325 0.961 1 0.691 0.598 -0.367
sewers -0.666 0.131 0.668 0.691 1 0.65 -0.581
shipping -0.696 0.22 0.686 0.598 0.65 1 -0.721
fisheries 0.718 -0.201 -0.471 -0.367 -0.581 -0.721 1

4.2. Univariate regressions

We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models).

We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the table below:

Human activity S N B H J
Aquaculture
City
Dredging - +
Industry
Sewers - - - -
Shipping
Fisheries: Dredge +
Fisheries: Net
Fisheries: Trap
Fisheries: Bottom-trawling
Adjusted \(R^{2}\) 0.17 0.01 0.01 0.13 0.05

Details of the regressions, with diagnostics and cross-validation, are summarized below.

Richness
## FULL MODEL
## Adjusted R2 is: 0.17
Fitting linear model: S ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.634e-16 0.08792 -4.133e-15 1
aquaculture 0.1269 0.1119 1.133 0.2598
city -0.03221 0.0961 -0.3352 0.7382
dredging -0.007244 0.1131 -0.06406 0.949
industry -0.1177 0.1364 -0.8627 0.3904
sewers -0.2185 0.1369 -1.596 0.1137
shipping 0.1539 0.1006 1.53 0.1292
fisheries 0.2022 0.09834 2.056 0.04238 * 
## RMSE from cross-validation: 0.9255828
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.27 1.09 1.28 1.54 1.55 1.14 1.11 

## REDUCED MODEL
## Adjusted R2 is: 0.17
Fitting linear model: S ~ sewers + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.797e-16 0.08781 -4.324e-15 1
sewers -0.3257 0.09453 -3.445 0.0008207 * * *
fisheries 0.1838 0.09453 1.944 0.05459 
## RMSE from cross-validation: 0.9169081
Variance Inflation Factors
  sewers fisheries
VIF 1.07 1.07

Density
## FULL MODEL
## Adjusted R2 is: 0
Fitting linear model: N ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.08e-16 0.0962 2.163e-15 1
aquaculture -0.03849 0.1225 -0.3142 0.754
city 0.1173 0.1051 1.116 0.2671
dredging -0.1121 0.1237 -0.9059 0.3672
industry -0.2061 0.1492 -1.381 0.1703
sewers 0.2321 0.1498 1.549 0.1245
shipping -0.1135 0.11 -1.032 0.3048
fisheries 0.05591 0.1076 0.5196 0.6045 
## RMSE from cross-validation: 1.049188
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.27 1.09 1.28 1.54 1.55 1.14 1.11 

## REDUCED MODEL
## Adjusted R2 is: 0.01
Fitting linear model: N ~ dredging
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.936e-16 0.09571 2.023e-15 1
dredging -0.1414 0.09615 -1.47 0.1444 
## RMSE from cross-validation: 1.00622
Variance Inflation Factors
  dredging
VIF 1

Biomass
## FULL MODEL
## Adjusted R2 is: 0
Fitting linear model: B ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.296e-17 0.0962 -8.623e-16 1
aquaculture -0.169 0.1225 -1.38 0.1708
city -0.1544 0.1051 -1.468 0.1452
dredging -0.01238 0.1237 -0.1 0.9205
industry 0.1813 0.1492 1.215 0.2272
sewers -0.3098 0.1498 -2.068 0.04121 *
shipping -0.1311 0.11 -1.192 0.2361
fisheries -0.04247 0.1076 -0.3947 0.6939 
## RMSE from cross-validation: 1.015242
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.27 1.09 1.28 1.54 1.55 1.14 1.11 

## REDUCED MODEL
## Adjusted R2 is: 0.01
Fitting linear model: B ~ sewers
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.956e-17 0.09577 -5.175e-16 1
sewers -0.1366 0.09622 -1.419 0.1587 
## RMSE from cross-validation: 0.992684
Variance Inflation Factors
  sewers
VIF 1

Diversity
## FULL MODEL
## Adjusted R2 is: 0.12
Fitting linear model: H ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.202e-16 0.09007 2.444e-15 1
aquaculture 0.1511 0.1147 1.318 0.1906
city -0.02873 0.09845 -0.2918 0.7711
dredging 0.1778 0.1158 1.535 0.1279
industry -0.1163 0.1397 -0.8322 0.4073
sewers -0.3091 0.1403 -2.204 0.02984 *
shipping 0.1232 0.103 1.196 0.2346
fisheries 0.03198 0.1008 0.3174 0.7516 
## RMSE from cross-validation: 0.9587176
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.27 1.09 1.28 1.54 1.55 1.14 1.11 

## REDUCED MODEL
## Adjusted R2 is: 0.13
Fitting linear model: H ~ sewers
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.089e-16 0.08981 2.326e-15 1
sewers -0.3702 0.09023 -4.103 8.035e-05 * * * 
## RMSE from cross-validation: 0.9377952
Variance Inflation Factors
  sewers
VIF 1

Evenness
## FULL MODEL
## Adjusted R2 is: 0.02
Fitting linear model: J ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.492e-17 0.09517 -3.669e-16 1
aquaculture 0.0703 0.1212 0.5802 0.5631
city 0.00113 0.104 0.01086 0.9914
dredging 0.2199 0.1224 1.797 0.07539
industry -0.1163 0.1476 -0.788 0.4326
sewers -0.2135 0.1482 -1.441 0.1527
shipping 0.002322 0.1089 0.02134 0.983
fisheries -0.1119 0.1065 -1.051 0.2957 
## RMSE from cross-validation: 1.098353
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.27 1.09 1.28 1.54 1.55 1.14 1.11 

## REDUCED MODEL
## Adjusted R2 is: 0.05
Fitting linear model: J ~ dredging + sewers
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.609e-17 0.09382 -3.846e-16 1
dredging 0.1796 0.1022 1.757 0.08177
sewers -0.2684 0.1022 -2.626 0.009928 * * 
## RMSE from cross-validation: 1.037299
Variance Inflation Factors
  dredging sewers
VIF 1.08 1.08

Annelid density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.07
Fitting generalized (poisson/log) linear model: annelids ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.356 0.01853 181.2 0 * * *
aquaculture 0.05295 0.02241 2.363 0.01814 *
city 0.1703 0.01764 9.652 4.797e-22 * * *
dredging -0.1515 0.03 -5.05 4.428e-07 * * *
industry -0.2985 0.03786 -7.885 3.133e-15 * * *
sewers 0.1864 0.03135 5.945 2.767e-09 * * *
shipping 0.04156 0.01873 2.219 0.02648 *
fisheries -0.1443 0.02466 -5.852 4.863e-09 * * * 
## Unbiased RMSE from cross-validation: 36.55575
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.3 1.09 1.32 1.62 1.61 1.14 1.12 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.07
Fitting generalized (poisson/log) linear model: annelids ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.356 0.01853 181.2 0 * * *
aquaculture 0.05295 0.02241 2.363 0.01814 *
city 0.1703 0.01764 9.652 4.797e-22 * * *
dredging -0.1515 0.03 -5.05 4.428e-07 * * *
industry -0.2985 0.03786 -7.885 3.133e-15 * * *
sewers 0.1864 0.03135 5.945 2.767e-09 * * *
shipping 0.04156 0.01873 2.219 0.02648 *
fisheries -0.1443 0.02466 -5.852 4.863e-09 * * * 
## Unbiased RMSE from cross-validation: 36.33155
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.3 1.09 1.32 1.62 1.61 1.14 1.12

Arthropod density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.18
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.612 0.01711 211.1 0 * * *
aquaculture -0.1314 0.02453 -5.356 8.518e-08 * * *
city 0.1532 0.01438 10.66 1.58e-26 * * *
dredging -0.1171 0.0233 -5.024 5.064e-07 * * *
industry -0.7129 0.03427 -20.8 4.076e-96 * * *
sewers 0.7624 0.0264 28.89 1.811e-183 * * *
shipping -0.1044 0.01618 -6.449 1.125e-10 * * *
fisheries 0.06578 0.01657 3.969 7.214e-05 * * * 
## Unbiased RMSE from cross-validation: 92.84347
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.23 1.05 1.22 1.94 1.98 1.1 1.12 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.18
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.612 0.01711 211.1 0 * * *
aquaculture -0.1314 0.02453 -5.356 8.518e-08 * * *
city 0.1532 0.01438 10.66 1.58e-26 * * *
dredging -0.1171 0.0233 -5.024 5.064e-07 * * *
industry -0.7129 0.03427 -20.8 4.076e-96 * * *
sewers 0.7624 0.0264 28.89 1.811e-183 * * *
shipping -0.1044 0.01618 -6.449 1.125e-10 * * *
fisheries 0.06578 0.01657 3.969 7.214e-05 * * * 
## Unbiased RMSE from cross-validation: 88.63896
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.23 1.05 1.22 1.94 1.98 1.1 1.12

Mollusc density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.18
Fitting generalized (poisson/log) linear model: molluscs ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.468 0.03018 81.8 0 * * *
aquaculture 0.1098 0.02823 3.889 0.0001006 * * *
city 0.224 0.02213 10.12 4.311e-24 * * *
dredging -0.08219 0.04036 -2.036 0.04171 *
industry 0.2458 0.03503 7.017 2.266e-12 * * *
sewers -0.3094 0.04285 -7.22 5.198e-13 * * *
shipping -0.271 0.04299 -6.305 2.883e-10 * * *
fisheries 0.07391 0.02413 3.063 0.002194 * * 
## Unbiased RMSE from cross-validation: 18.34011
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.29 1.15 1.49 1.51 1.39 1.19 1.1 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.18
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.612 0.01711 211.1 0 * * *
aquaculture -0.1314 0.02453 -5.356 8.518e-08 * * *
city 0.1532 0.01438 10.66 1.58e-26 * * *
dredging -0.1171 0.0233 -5.024 5.064e-07 * * *
industry -0.7129 0.03427 -20.8 4.076e-96 * * *
sewers 0.7624 0.0264 28.89 1.811e-183 * * *
shipping -0.1044 0.01618 -6.449 1.125e-10 * * *
fisheries 0.06578 0.01657 3.969 7.214e-05 * * * 
## Unbiased RMSE from cross-validation: 89.01111
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries
VIF 1.23 1.05 1.22 1.94 1.98 1.1 1.12

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